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05/22/2023
Conditional normalization in time series analysis
Time series often reflect variation associated with other related variab...
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03/30/2023
Cross-temporal Probabilistic Forecast Reconciliation
Forecast reconciliation is a post-forecasting process that involves tran...
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10/13/2022
Anomaly detection in dynamic networks
Detecting anomalies from a series of temporal networks has many applicat...
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11/13/2021
LoMEF: A Framework to Produce Local Explanations for Global Model Time Series Forecasts
Global Forecasting Models (GFM) that are trained across a set of multipl...
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08/08/2021
A Look at the Evaluation Setup of the M5 Forecasting Competition
Forecast evaluation plays a key role in how empirical evidence shapes th...
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07/28/2021
MSTL: A Seasonal-Trend Decomposition Algorithm for Time Series with Multiple Seasonal Patterns
The decomposition of time series into components is an important task th...
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07/27/2021
Detection of cybersecurity attacks through analysis of web browsing activities using principal component analysis
Organizations such as government departments and financial institutions ...
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05/14/2021
Monash Time Series Forecasting Archive
Many businesses and industries nowadays rely on large quantities of time...
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02/22/2021
Manifold learning with approximate nearest neighbors
Manifold learning algorithms are valuable tools for the analysis of high...
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10/29/2020
Modern strategies for time series regression
This paper discusses several modern approaches to regression analysis in...
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10/21/2020
Model selection in reconciling hierarchical time series
Model selection has been proven an effective strategy for improving accu...
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10/02/2020
Visualizing probability distributions across bivariate cyclic temporal granularities
Deconstructing a time index into time granularities can assist in explor...
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09/24/2020
Forecasting for Social Good
Forecasting plays a critical role in the development of organisational b...
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08/02/2020
Principles and Algorithms for Forecasting Groups of Time Series: Locality and Globality
Forecasting groups of time series is of increasing practical importance,...
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06/03/2020
Hierarchical forecast reconciliation with machine learning
Hierarchical forecasting methods have been widely used to support aligne...
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12/01/2019
Machine learning applications in time series hierarchical forecasting
Hierarchical forecasting (HF) is needed in many situations in the supply...
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08/12/2019
Anomaly Detection in High Dimensional Data
The HDoutliers algorithm is a powerful unsupervised algorithm for detect...
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03/07/2019
GRATIS: GeneRAting TIme Series with diverse and controllable characteristics
The explosion of time series data in recent years has brought a flourish...
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02/17/2019
A feature-based framework for detecting technical outliers in water-quality data from in situ sensors
Outliers due to technical errors in water-quality data from in situ sens...
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01/29/2019
A new tidy data structure to support exploration and modeling of temporal data
Mining temporal data for information is often inhibited by a multitude o...
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10/31/2018
A framework for automated anomaly detection in high frequency water-quality data from in situ sensors
River water-quality monitoring is increasingly conducted using automated...
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10/30/2018
Predicting Sediment and Nutrient Concentrations in Rivers Using High Frequency Water Quality Surrogates
A particular focus of water-quality monitoring is the concentrations of ...
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10/23/2018